class_docstr: Feature fusion block.
docstr: Init.
  
  Args:
      features (int): number of features
docstr_tok: ["Init",".","Args",":","features","(","int",")",":","number","of","features"]
code: def __init__(
      self,
      features,
      activation,
      deconv=False,
      bn=False,
      expand=False,
      align_corners=True,
  ):
      """Init.
  
      Args:
          features (int): number of features
      """
      super(FeatureFusionBlock_custom, self).__init__()
  
      self.deconv = deconv
      self.align_corners = align_corners
  
      self.groups = 1
  
      self.expand = expand
      out_features = features
      if self.expand == True:
          out_features = features // 2
  
      self.out_conv = nn.Conv2d(
          features,
          out_features,
          kernel_size=1,
          stride=1,
          padding=0,
          bias=True,
          groups=1,
      )
  
      self.resConfUnit1 = ResidualConvUnit_custom(
          features, activation, bn
      )
      self.resConfUnit2 = ResidualConvUnit_custom(
          features, activation, bn
      )
  
      self.skip_add = nn.quantized.FloatFunctional()
  
imports: import torch
  import torch.nn
  from .vit import _make_pretrained_vitb16_384, _make_pretrained_vitb_rn50_384, _make_pretrained_vitl16_384
label_desc: Use image node_id 1 to create a new FeatureFusionBlock_custom object from inherited base classes: nn with example: obj = FeatureFusionBlock_custom(features, activation, deconv, bn, expand, align_corners)